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dbt Labs dbt-Analytics-Engineering Exam Syllabus Topics:

dbt Labs dbt Analytics Engineering Certification Sample Questions:

1. (Multiple Select)

A) Create views on top of dbt model tables, providing a stable abstraction layer for the Bl tools.
B) Avoid refactoring dbt models that are directly queried by external tools.
C) Instruct dbt to rename columns rather than dropping and recreating them during updates.
D) Establish a naming convention in dbt that external tools can rely upon.

2. A sudden data quality issue occurs in production. You need to quickly reproduce the problem in a lower environment but retain a point-in-time snapshot of production dat a. Which strategies might you employ? Create a backup of the production database and restore it to a separate development or testing database.

A) staging area.
B) Use dbt snapshots to capture critical production tables during a defined timeframe for later use in troubleshooting.
C) A combination of the above, depending on time constraints and available resources.
D) Utilize data replication features (if supported by your data warehouse) to create a near real-time mirror of production data in a

3. (Multiple Select)

A) Manually examine the compiled SQL files.
B) dbt test
C) ref() and source() functions
D) Snapshots
E) Write a custom Python script to parse dbt models.
F) Inspect the manifest.json file.
G) Use the dbt debug command.
H) Jinja templating
I) dbt docs

4. You've configured your project to include both schema and data tests in the generated documentation. A user reports the tests are visible but don't show any execution results (pass/fail status). Why might this be happening?

A) The type of test you're using is not supported for displaying results in the documentation.
B) The project settings are explicitly configured to hide test results in the documentation.
C) The documentation generator has a known limitation on how test results are displayed.
D) Your data pipeline hasn't been executed recently, so there are no recent test results.

5. (Multiple Select)

A) Changes in the structure or content of the baseline dataset itself.
B) Natural drift in the distribution of real-world data compared to the static baseline.
C) Changes in business logic lead to intentional deviations from the baseline.
D) A change in database vendor for your data warehouse.

Solutions: